Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Gingiris-1031/gingiris-skills --skill ai-launch-playbookgit clone --depth 1 https://github.com/Gingiris-1031/gingiris-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gingiris-1031/gingiris-skills/ai-launch-playbook)<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/ai-launch-playbook"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/ai-launch-playbook/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/ai-launch-playbook"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/ai-launch-playbook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.01450 |
| Opus 5 | $0.00027 | $0.00725 |
| Sonnet 5 | $0.00011 | $0.00290 |
| Haiku 4.5 | $0.00005 | $0.00145 |
Grade A, and why
ai-launch-playbook scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚠️ 2C 产品慎用 Product Hunt
本 skill 默认 dev / maker / B2B 受众。2C 消费品 / 教育 / 应用 / 游戏：Product Hunt 受众是 maker/科技人，与普通消费者错配、ROI 极低——最多发一次拿个 badge。请把精力换到 垂类社区（Reddit / 小红书 / 知乎 / Naver 카페）+ 短视频（TikTok / 抖音 / YouTube）+ 垂直 KOL，并用"免费产品当钩子"做转化。完整 2C 渠道数据库（各国 MAU + 公开来源）+ 三路 Launch 分叉见 → gingiris-seo-geo/references/2c-adaptation.md（及 gingiris-launch）。
📦 Install
clawhub install ai-launch-playbook
What you get after installing:
- AI-specific GTM strategy with hype cycle management and waitlist tactics
- Multi-market rollout playbook for maximum day-one global impact
- Real case studies from Manus, Devin, and AFFiNE breakout launches
AI Product Launch Playbook
Complete guide for launching AI products globally, with real case studies.
What You Get
- AI product positioning strategies
- Global launch timing and market selection
- KOL and influencer outreach for AI products
- Community building in AI/ML spaces
- Case studies: Manus, Devin, AFFiNE
What Actually Made Manus & Devin Travel (2026 analysis update)
Four forces, in causal order — skipping the first breaks the other three:
- Product depth as the narrative floor. Hype without substance decays in ~72 hours; the story can only stand as tall as the product is deep.
- Reproducibility sets the emotional ceiling. Users could re-run Manus demos themselves, so testimonials read as discovery rather than marketing — one creator's genuine recommendation pulled 2.8M views. Devin's non-reproducible demos drew hedged, polite coverage; the same creator's favor-share managed roughly 20 likes. Design demos for screen-recording and re-creation.
- The influencer flywheel. Founders, operators, and investors with real followings are each worth ten commissioned posts. Seed genuine surprise, never obligation — the 2.8M-vs-20 gap above is exactly that difference.
- The platform relay race. Days 0–3: X/Twitter ignites the topic. Days 3–10: YouTube and Substack deepen it. Day 10+: Reddit and HN keep the long tail alive. Plan the handoffs, not just the spark.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 115 lines · 54 tokens per session scan A bfb219a7ede6
ai-launch-playbook is a skill published in the GitHub repository Gingiris-1031/gingiris-skills (79 stars, last pushed 5d ago), licensed MIT. It adds 54 tokens to every session and 1,450 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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